Fast VVC Test Zone Search and Affine Motion Estimation Using Machine Learning

Ramiro Viana, Marta Loose, Ruhan A. Conceição, Marcelo Schiavon Porto, Guilherme Corrêa, Luciano Agostini

Abstract: As the demand for video transmission surges on remote work, education, and streaming services, the need for continuous advancements in video encoding technologies becomes increasingly evident. Adapting to the evolving requirements of efficient video delivery and consumption necessitates ongoing development and enhancement in video encoding standards, with Versatile Video Coding (VVC) emerging as a notable example. This paper provides an overview of key algorithms within InterFrame prediction of VVC, mainly focusing on the Test Zone Search (TZS) and the Affine Motion Estimation (AME), two of the most computationally intensive tools inside the VVC. Furthermore, this paper introduces a fast TZS and AME approach using Machine Learning, specifically employing Decision Trees. The proposed approach achieved an average reduction of over $\mathbf{2 0 \%}$ in total VVC encoding time while maintaining less than a 1 % impact on BD-BR coding efficiency.